| Author | Porntip Kijwanichprasert |
| Call Number | AIT Thesis no. ISE-96-13 |
| Subject(s) | Assembly-line balancing
|
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of
Engineering, School of Advanced Technologies |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. ISE-96-13 |
| Abstract | Due to high competition, a high rate of technological change and changing markets,
most companies should not produce the same model in high volume. They should produce
small lots of a variety of products. Therefore, responses to the market demands, shorter
product life cycles and many production changeovers result in a large number of learning
cycles for operational workers when companies would like to produce a new model or product.
Consequently, we might find that consideration of learning is of more practical significance
for balancing assembly lines to reduce cost of labor and improve productivity. However, little
research has been done which views line balancing as truly integrated with learning, even if
learning impacts on assembly line performance and cost has been recognized. Most research
has viewed the assembly line balancing problem in purely technical dimensions. By enlarging
the scope of analysis of the assembly line balancing system, the effect of learning can be
considered.
The overall objective of this study is to analyze the line balance of production
assembly lines for both single and multiple models, in the presence of learning effects. The
corresponding objectives are to develop mathematical (Linear Integer Programming) models
for balancing both single and multiple models of assembly lines, considering short and long
cycles and; to analyze various balancing models for the developed models in presence of some
characteristic learning rates.
In this research, a model for a single model with the same learning rate (all tasks have
the same learning rate), a single model with different learning rates (all tasks do not have the
same learning rate), a multi-model with the same learning rate (all tasks of all models have the
same learning rate), and a multi-model with different learning rates (all tasks of all models do
not have the same learning rate) will be developed. The results from these new models will be
compared with the results from the traditional models. Moreover, for the cases of a multi-model, balancing the assembly line by one configuration for all models will be compared with
balancing the assembly line by separate configurations for each model. The models which is
formulated will be tested by using existing data. |
| Year | 1996 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. ISE-96-13 |
| Type | Thesis |
| School | School of Advanced Technologies (SAT) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Nagarur, Nagendra N.; |
| Examination Committee(s) | Bohez, Erik L. J.;Anulark Pinnoi; |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 1996 |
